Have you ever wondered if there is a way to predict customer churn so early on that you have ample of time to put in place a strategy to prevent it?
This article explains what ‘Churn Prediction’ means and how it can be done using Machine Learning and Predictive Analytics to decease Customer Churn Rate and increase Customer Retention.
The definition of “Churn’ is different for different industries. For an E-Commerce or a telecommunication company, it refers to the loss of clients or customers while, for a subscription-based business, churn means the loss of subscribers. In a nutshell, Churn is when a consumer stops purchasing and doing business with a corporation or engaging with a brand. Essentially, a customer leaves a company for its competitor.
As established in the infamous research article by Fred Reichheld titled Prescription for Cutting Costs, it is much more economical to retain existing customers than to acquire brand new ones.
Our markets are becoming increasingly saturated with numerous options for customers to choose from. “In financial services, for example, a 5% increase in customer retention produces more than a 25% increase in profit. Why? Return customers tend to buy more from a company over time. As they do, your operating costs to serve them decline.” (Fred Reichheld). According to a Report published by the World Academy of Science, Engineering and Technology, many organisations have gradually shifted their marketing approach from ‘product-centric’ to ‘customer-centric’.
Therefore, Churn Prediction, using Machine Learning can be enormously useful for retaining customers, clients and subscribers.
The formula for Customer Churn Prediction is,
Customer Churn Rate can be predicted quite accurately using Machine Learning algorithms and Predictive Analytics.
To predict the Churn Rate effectively one needs a suitable Churn Prediction Model and access to-
Predictive Analytics, using Market Research can be immensely helpful in decreasing Churn and thereby, increasing Customer Retention.
For instance, imagine that you’re a telecom provider. You have collected the survey responses of a customer, Mr. X. After 6 months, you go back to your database to check if Mr. X is still a customer. If he isn’t, you train the database to acknowledge that he isn’t a customer any longer. You do this over a hundred records. After that, the machine learns that if this is how a customer is answering a survey, there is an x percent chance that that he will churn.
According to a study published in The Journal of Marketing Research Vol. XLIII (Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models), “in the cellular phone industry, annual churn rates range from 23.4% (Wireless Review 2000) to 46% (Fitchard 2002).”
Because of the plethora of choices available to a consumer today, Churn is a colossal issue for the telecom, financial, e-commerce, insurance and subscription businesses. An effective way to reduce Churn is to predict and identify the customers who are most likely to Churn and target them with incentives to convince them to stay. This can be done extremely well using a Predictive Analytic software.
One of the reasons why Predictive Analytics is so effective in measuring Customer Churn Rate is that with some
it can identify the at-risk customers right away, leaving the marketer with ample of time to take appropriate action.
An example of how Predictive Analytics and Machine Learning(backed by Market Research) is being used to manage Churn is how Netflix proactively personalises its content based on the historical and behavioural data of its users and predicts and solves user queries in real time.
It is undeniable that for an organisation to prosper, it needs to be acutely aware of the motivations of its customers. Analysing the Customer Churn Rate, calculated using Predictive Analytics and Machine Learning is one of the best ways to achieve this and gain a competitive edge in the market.